SYSTEMS AND METHODS FOR APPLYING DEEP LEARNING TO DATA

摘要

A computing system is provided in which sparse vectors is obtained. Each vector represents a single entity, and has at least ten thousand elements each of which represents an entity feature. Less than ten percent of the elements in each vector is present in the input data. The vectors are applied to a plurality of denoising autoencoders. Each respective autoencoder, other than the final autoencoder, feeds intermediate values as a function of (i) a weight coefficient matrix and bias vector associated with the respective autoencoder and (ii) input values received by the autoencoder, into another autoencoder. The final autoencoder outputs a dense vector, consisting of less than 1000 elements, for each sparse vector thereby forming a plurality of dense vectors. A post processor engine is trained on the plurality of dense vectors causing the engine to predict a future change in a value for a feature for a test entity.